
# import libraries
import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
from scipy import stats


# Load the Excel dataset
df = pd.read_excel('D:\\DATA ANALYTICS PROJECT\\FASHION_DATA.xlsx')


# print dataframe
df


# print columns present
df.columns

Index(['ID', 'Consumer ID', 'Gender', 'Age Group', 'Income ($)', 'Occupation',
       'Preferred Style', 'Favorite Brand', 'Purchase Frequency (per month)',
       'Avg Spend per Purchase ($)', 'Preferred Material', 'Size',
       'Color Preference', 'Online Shopping Frequency', 'Return Rate (%)',
       'Satisfaction Rating (1-5)', 'Influencer Follow (Yes/No)',
       'Discounts Effectiveness (1-5)', 'Sustainable Preference (Yes/No)',
       'Last Purchase Category', 'Last Purchase Date',
       'Review Written (Yes/No)'],
      dtype='object')


# Check the data types of each column
print(df.dtypes)

ID                                          int64
Consumer ID                                object
Gender                                     object
Age Group                                  object
Income ($)                                  int64
Occupation                                 object
Preferred Style                            object
Favorite Brand                             object
Purchase Frequency (per month)              int64
Avg Spend per Purchase ($)                  int64
Preferred Material                         object
Size                                       object
Color Preference                           object
Online Shopping Frequency                  object
Return Rate (%)                             int64
Satisfaction Rating (1-5)                 float64
Influencer Follow (Yes/No)                 object
Discounts Effectiveness (1-5)               int64
Sustainable Preference (Yes/No)            object
Last Purchase Category                     object
Last Purchase Date                 datetime64[ns]
Review Written (Yes/No)                    object
dtype: object


# Check for missing values in each column
missing_values = df.isnull().sum()

# Display the count of missing values per column
print(missing_values)

ID                                 0
Consumer ID                        0
Gender                             0
Age Group                          0
Income ($)                         0
Occupation                         0
Preferred Style                    0
Favorite Brand                     0
Purchase Frequency (per month)     0
Avg Spend per Purchase ($)         0
Preferred Material                 0
Size                               0
Color Preference                   0
Online Shopping Frequency          0
Return Rate (%)                    0
Satisfaction Rating (1-5)          0
Influencer Follow (Yes/No)         0
Discounts Effectiveness (1-5)      0
Sustainable Preference (Yes/No)    0
Last Purchase Category             0
Last Purchase Date                 0
Review Written (Yes/No)            0
dtype: int64


# checking for outliers using z score

import pandas as pd
import numpy as np
from scipy.stats import zscore


# Apply Z-score to numeric columns
z_scores = np.abs(df.select_dtypes(include=[np.number]).apply(zscore))

# Set a threshold for detecting outliers 
threshold = 3

# Find the outliers
outliers = (z_scores > threshold)

# Display the outliers (True = Outlier)
print(outliers)


# Sum the number of outliers per column
print("Outliers per column:")
print(outliers.sum())

        ID  Income ($)  Purchase Frequency (per month)  \
0    False       False                           False   
1    False       False                           False   
2    False       False                           False   
3    False       False                           False   
4    False       False                           False   
..     ...         ...                             ...   
995  False       False                           False   
996  False       False                           False   
997  False       False                           False   
998  False       False                           False   
999  False       False                           False   

     Avg Spend per Purchase ($)  Return Rate (%)  Satisfaction Rating (1-5)  \
0                         False            False                      False   
1                         False            False                      False   
2                         False             True                      False   
3                         False            False                      False   
4                         False             True                      False   
..                          ...              ...                        ...   
995                       False            False                      False   
996                       False            False                      False   
997                       False            False                      False   
998                       False            False                      False   
999                       False            False                      False   

     Discounts Effectiveness (1-5)  
0                            False  
1                            False  
2                            False  
3                            False  
4                            False  
..                             ...  
995                          False  
996                          False  
997                          False  
998                          False  
999                          False  

[1000 rows x 7 columns]
Outliers per column:
ID                                 0
Income ($)                         0
Purchase Frequency (per month)     0
Avg Spend per Purchase ($)         0
Return Rate (%)                   11
Satisfaction Rating (1-5)         13
Discounts Effectiveness (1-5)      0
dtype: int64


# Filter and display outliers for specific columns
outlier_indices = outliers.any(axis=1)
outlier_data = df[outlier_indices]

# Display outlier data for Return Rate (%) and Satisfaction Rating (1-5)
print(outlier_data[['Return Rate (%)', 'Satisfaction Rating (1-5)']])

     Return Rate (%)  Satisfaction Rating (1-5)
2                 15                        4.8
4                 20                        4.6
10                15                        4.8
14                15                        4.5
22                15                        4.3
26                15                        4.5
34                15                        4.3
38                15                        4.5
46                15                        4.3
556               10                        3.0
559               15                        4.0
573               12                        3.0
582               10                        3.0
585               15                        4.0
594                3                        3.0
603                3                        3.0
606                2                        3.0
621               10                        3.0
633               10                        3.0
645               10                        3.0
657               10                        3.0
662                6                        3.0
674                6                        3.0
722                1                        3.0


# Remove outliers from the original DataFrame
df = df[~outlier_indices]

df


# Normalization
scaler = StandardScaler()
df[['Income ($)', 'Avg Spend per Purchase ($)', 'Purchase Frequency (per month)']] = scaler.fit_transform(
    df[['Income ($)', 'Avg Spend per Purchase ($)', 'Purchase Frequency (per month)']]
)

C:\Users\hp\AppData\Local\Temp\ipykernel_19500\2282267253.py:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df[['Income ($)', 'Avg Spend per Purchase ($)', 'Purchase Frequency (per month)']] = scaler.fit_transform(

df


# Descriptive statistics
print(df.describe())

                ID    Income ($)  Purchase Frequency (per month)  \
count   976.000000  9.760000e+02                    9.760000e+02   
mean    503.081967 -2.329648e-16                    1.274026e-16   
min       1.000000 -1.778798e+00                   -1.109663e+00   
25%     253.750000 -1.289059e+00                   -1.109663e+00   
50%     497.500000  4.815371e-01                   -4.163011e-01   
75%     756.250000  8.582596e-01                    9.704221e-01   
max    1000.000000  1.234982e+00                    1.663784e+00   
std     288.281724  1.000513e+00                    1.000513e+00   

       Avg Spend per Purchase ($)  Return Rate (%)  Satisfaction Rating (1-5)  \
count                9.760000e+02       976.000000                 976.000000   
mean                 2.912060e-17         4.628074                   4.521311   
min                 -1.505264e+00         0.000000                   4.000000   
25%                 -1.010149e+00         3.000000                   4.200000   
50%                  2.276378e-01         5.000000                   4.500000   
75%                  6.608633e-01         6.000000                   4.900000   
max                  2.208097e+00        12.000000                   5.000000   
std                  1.000513e+00         2.602561                   0.371134   

       Discounts Effectiveness (1-5)             Last Purchase Date  
count                     976.000000                            976  
mean                        4.143443  2025-02-25 10:44:45.245901312  
min                         2.000000            2024-06-20 00:00:00  
25%                         4.000000            2024-10-05 00:00:00  
50%                         4.000000            2024-10-31 00:00:00  
75%                         5.000000            2025-08-15 06:00:00  
max                         6.000000            2025-12-15 00:00:00  
std                         0.822702                            NaN


# Funnel Analysis

import matplotlib.pyplot as plt

# Define funnel stages
funnel_stages = {
    'Visited Site': len(df),  # Total number of consumers
    'Added to Cart': df['Purchase Frequency (per month)'][df['Purchase Frequency (per month)'] > 0].count(),
    'Initiated Checkout': df[df['Avg Spend per Purchase ($)'] > 0].shape[0],
    'Completed Purchase': df[df['Purchase Frequency (per month)'] > 0].shape[0]
}

# Create a DataFrame for funnel analysis
funnel_df = pd.DataFrame(funnel_stages.items(), columns=['Stage', 'Count'])

# Calculate conversion rates
funnel_df['Conversion Rate'] = funnel_df['Count'] / funnel_df['Count'].max()

# Plotting the funnel
plt.figure(figsize=(10, 6))
plt.bar(funnel_df['Stage'], funnel_df['Count'], color='skyblue')
plt.title('Funnel Analysis')
plt.xlabel('Funnel Stage')
plt.ylabel('Number of Users')
plt.xticks(rotation=45)
plt.grid(axis='y')

# Add counts above bars
for i, v in enumerate(funnel_df['Count']):
    plt.text(i, v + 0.5, str(v), ha='center', va='bottom')

plt.tight_layout()
plt.show()

# Display the funnel DataFrame
print(funnel_df)

                Stage  Count  Conversion Rate
0        Visited Site    976         1.000000
1       Added to Cart    436         0.446721
2  Initiated Checkout    526         0.538934
3  Completed Purchase    436         0.446721


# Example: Deeper funnel analysis based on Gender

import pandas as pd
import matplotlib.pyplot as plt


# Calculate counts for each funnel stage by Gender
funnel_by_gender = df.groupby('Gender').agg(
    Visited_Site=('ID', 'count'),
    Added_to_Cart=('Purchase Frequency (per month)', lambda x: (x > 0).sum()),
    Initiated_Checkout=('Avg Spend per Purchase ($)', lambda x: (x > 0).count()),
    Completed_Purchase=('Purchase Frequency (per month)', lambda x: (x > 0).sum())
).reset_index()

# Calculate conversion rates
funnel_by_gender['Conversion_Rate_Added'] = funnel_by_gender['Added_to_Cart'] / funnel_by_gender['Visited_Site']
funnel_by_gender['Conversion_Rate_Checkout'] = funnel_by_gender['Initiated_Checkout'] / funnel_by_gender['Added_to_Cart']
funnel_by_gender['Conversion_Rate_Completed'] = funnel_by_gender['Completed_Purchase'] / funnel_by_gender['Initiated_Checkout']

# Visualization: Separate Bar Plots for Each Funnel Stage
funnel_stages = ['Visited_Site', 'Added_to_Cart', 'Initiated_Checkout', 'Completed_Purchase']
funnel_by_gender.set_index('Gender')[funnel_stages].plot(kind='bar', figsize=(10, 6))
plt.title('Funnel Analysis by Gender')
plt.ylabel('Count')
plt.xlabel('Gender')
plt.xticks(rotation=0)
plt.legend(title='Funnel Stage')
plt.show()


import pandas as pd
import matplotlib.pyplot as plt
import plotly.express as px

# Calculate counts for each funnel stage by Gender
funnel_by_gender = df.groupby('Gender').agg(
    Visited_Site=('ID', 'count'),
    Added_to_Cart=('Purchase Frequency (per month)', lambda x: (x > 0).sum()),
    Initiated_Checkout=('Avg Spend per Purchase ($)', lambda x: (x > 0).count()),
    Completed_Purchase=('Purchase Frequency (per month)', lambda x: (x > 0).sum())
).reset_index()

# Calculate conversion rates
funnel_by_gender['Conversion_Rate_Added'] = funnel_by_gender['Added_to_Cart'] / funnel_by_gender['Visited_Site']
funnel_by_gender['Conversion_Rate_Checkout'] = funnel_by_gender['Initiated_Checkout'] / funnel_by_gender['Added_to_Cart']
funnel_by_gender['Conversion_Rate_Completed'] = funnel_by_gender['Completed_Purchase'] / funnel_by_gender['Initiated_Checkout']

# Visualization 1: Funnel Chart for Conversion Rates using Plotly
funnel_data = funnel_by_gender.melt(id_vars='Gender', 
    value_vars=['Conversion_Rate_Added', 'Conversion_Rate_Checkout', 'Conversion_Rate_Completed'],
    var_name='Funnel_Stage', value_name='Conversion_Rate')

# Create a funnel chart for each gender
for gender in funnel_data['Gender'].unique():
    data_gender = funnel_data[funnel_data['Gender'] == gender]
    fig = px.funnel(data_gender, y='Funnel_Stage', x='Conversion_Rate', title=f'Conversion Funnel for {gender}')
    fig.show()

# Visualization 2: Pie Charts for Conversion Rates by Gender
# Example for Gender-based Pie Chart for Completed Purchase Conversion Rates
for gender in funnel_by_gender['Gender']:
    values = funnel_by_gender[funnel_by_gender['Gender'] == gender][['Conversion_Rate_Added', 'Conversion_Rate_Checkout', 'Conversion_Rate_Completed']].values[0]
    labels = ['Added to Cart', 'Initiated Checkout', 'Completed Purchase']
    
    plt.figure(figsize=(6, 6))
    plt.pie(values, labels=labels, autopct='%1.1f%%', startangle=90, colors=['#66b3ff', '#ff9999', '#99ff99'])
    plt.title(f'Conversion Rates for {gender}')
    plt.axis('equal')  # Equal aspect ratio ensures the pie chart is a circle.
    plt.show()

# Display the detailed funnel by gender
print(funnel_by_gender)

   Gender  Visited_Site  Added_to_Cart  Initiated_Checkout  \
0  Female           486            258                 486   
1    Male           490            178                 490   

   Completed_Purchase  Conversion_Rate_Added  Conversion_Rate_Checkout  \
0                 258               0.530864                  1.883721   
1                 178               0.363265                  2.752809   

   Conversion_Rate_Completed  
0                   0.530864  
1                   0.363265


# Exploratory Data Analysis
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns


# Exclude specified columns
df1 = df.drop(columns=['Occupation', 'ID', 'Consumer ID', 'Favorite Brand'])

# Define numerical and categorical columns
numerical_cols = df1.select_dtypes(include=['float64', 'int64']).columns
categorical_cols = df1.select_dtypes(include=['object']).columns

# Set the style for seaborn
sns.set(style='whitegrid', palette='bright')

# Univariate Analysis
def univariate_analysis(df1):
    print("### Univariate Analysis ###")
    
    # Summary statistics
    print("Summary Statistics:")
    print(df1.describe())
    
    # Distribution plots for numerical columns
    for col in numerical_cols:
        plt.figure(figsize=(10, 5))
        sns.histplot(df[col], bins=30, kde=True, color='skyblue')
        plt.title(f'Distribution of {col}', fontsize=16)
        plt.xlabel(col, fontsize=14)
        plt.ylabel('Frequency', fontsize=14)
        plt.grid()
        plt.show()
        
    # Count plots for categorical columns
    for col in categorical_cols:
        plt.figure(figsize=(10, 5))
        sns.countplot(data=df1, x=col, palette='viridis')
        plt.title(f'Count of {col}', fontsize=16)
        plt.xticks(rotation=45, fontsize=12)
        plt.ylabel('Count', fontsize=14)
        plt.grid()
        plt.show()

# Bivariate Analysis
def bivariate_analysis(df1):
    print("### Bivariate Analysis ###")
    
    # Scatter plots for numerical variables
    for i in range(len(numerical_cols)):
        for j in range(i + 1, len(numerical_cols)):
            plt.figure(figsize=(10, 5))
            sns.scatterplot(data=df1, x=numerical_cols[i], y=numerical_cols[j], color='orange', edgecolor='black')
            plt.title(f'Scatter plot of {numerical_cols[i]} vs {numerical_cols[j]}', fontsize=16)
            plt.xlabel(numerical_cols[i], fontsize=14)
            plt.ylabel(numerical_cols[j], fontsize=14)
            plt.grid()
            plt.show()
    
    # Box plots for numerical vs categorical
    for cat_col in categorical_cols:
        for num_col in numerical_cols:
            plt.figure(figsize=(10, 5))
            sns.boxplot(data=df1, x=cat_col, y=num_col, palette='pastel')
            plt.title(f'Box plot of {num_col} by {cat_col}', fontsize=16)
            plt.xticks(rotation=45, fontsize=12)
            plt.ylabel(num_col, fontsize=14)
            plt.grid()
            plt.show()

# Multivariate Analysis
def multivariate_analysis(df1):
    print("### Multivariate Analysis ###")
    
    # Pairplot for numerical variables
    sns.pairplot(df1[numerical_cols], palette='husl')
    plt.title('Pairplot of Numerical Features', fontsize=16)
    plt.show()
    
    # Correlation heatmap
    plt.figure(figsize=(12, 8))
    correlation = df1[numerical_cols].corr()
    
   
    # Use a more vibrant colormap
    sns.heatmap(correlation, annot=True, cmap='viridis', fmt='.2f', square=True, linewidths=0.5, cbar_kws={"shrink": .8})

    plt.title('Correlation Heatmap', fontsize=16)
    plt.xticks(rotation=45)
    plt.yticks(rotation=45)
    plt.tight_layout()  
    plt.show()

# Run EDA functions
univariate_analysis(df1)
bivariate_analysis(df1)
multivariate_analysis(df1)

### Univariate Analysis ###
Summary Statistics:
         Income ($)  Purchase Frequency (per month)  \
count  9.760000e+02                    9.760000e+02   
mean  -2.329648e-16                    1.274026e-16   
min   -1.778798e+00                   -1.109663e+00   
25%   -1.289059e+00                   -1.109663e+00   
50%    4.815371e-01                   -4.163011e-01   
75%    8.582596e-01                    9.704221e-01   
max    1.234982e+00                    1.663784e+00   
std    1.000513e+00                    1.000513e+00   

       Avg Spend per Purchase ($)  Return Rate (%)  Satisfaction Rating (1-5)  \
count                9.760000e+02       976.000000                 976.000000   
mean                 2.912060e-17         4.628074                   4.521311   
min                 -1.505264e+00         0.000000                   4.000000   
25%                 -1.010149e+00         3.000000                   4.200000   
50%                  2.276378e-01         5.000000                   4.500000   
75%                  6.608633e-01         6.000000                   4.900000   
max                  2.208097e+00        12.000000                   5.000000   
std                  1.000513e+00         2.602561                   0.371134   

       Discounts Effectiveness (1-5)             Last Purchase Date  
count                     976.000000                            976  
mean                        4.143443  2025-02-25 10:44:45.245901312  
min                         2.000000            2024-06-20 00:00:00  
25%                         4.000000            2024-10-05 00:00:00  
50%                         4.000000            2024-10-31 00:00:00  
75%                         5.000000            2025-08-15 06:00:00  
max                         6.000000            2025-12-15 00:00:00  
std                         0.822702                            NaN

C:\Users\hp\AppData\Local\Temp\ipykernel_19500\3464606067.py:38: FutureWarning:


Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

C:\Users\hp\AppData\Local\Temp\ipykernel_19500\3464606067.py:38: FutureWarning:


Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

C:\Users\hp\AppData\Local\Temp\ipykernel_19500\3464606067.py:38: FutureWarning:


Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

C:\Users\hp\AppData\Local\Temp\ipykernel_19500\3464606067.py:38: FutureWarning:


Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

C:\Users\hp\AppData\Local\Temp\ipykernel_19500\3464606067.py:38: FutureWarning:


Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

C:\Users\hp\AppData\Local\Temp\ipykernel_19500\3464606067.py:38: FutureWarning:


Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

C:\Users\hp\AppData\Local\Temp\ipykernel_19500\3464606067.py:38: FutureWarning:


Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

C:\Users\hp\AppData\Local\Temp\ipykernel_19500\3464606067.py:38: FutureWarning:


Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

C:\Users\hp\AppData\Local\Temp\ipykernel_19500\3464606067.py:38: FutureWarning:


Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.


# Feature Engineering
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, LabelEncoder
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import mean_squared_error, confusion_matrix, accuracy_score, classification_report
import warnings

warnings.filterwarnings('ignore')


# Dropping unnecessary columns
df2 = df.drop(columns=['Occupation', 'ID', 'Customer ID', 'Favorite Brand'], errors='ignore')

# Convert date columns to numerical if any
for col in df2.select_dtypes(include=['datetime64[ns]', 'datetime64[ns, UTC]']):
    df2[col] = df2[col].astype('int64') // 10**9  # Convert to seconds since epoch

# Create a binary feature for high spenders
df2['High_Spender'] = np.where(df2['Avg Spend per Purchase ($)'] > df2['Avg Spend per Purchase ($)'].median(), 1, 0)

# Create a total income feature
df2['Total_Income'] = df2['Income ($)'] * df2['Purchase Frequency (per month)']

# Encoding categorical features (if any)
df2= pd.get_dummies(df2, drop_first=True)

# Splitting data into features and target
X = df2.drop(columns=['High_Spender'], errors='ignore')
y = df2['High_Spender']

# Scaling numerical features
scaler = StandardScaler()
numerical_cols = X.select_dtypes(include=['float64', 'int64']).columns
X[numerical_cols] = scaler.fit_transform(X[numerical_cols])

# Splitting the dataset
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Building a Random Forest Classifier model
model = RandomForestClassifier(random_state=42)
model.fit(X_train, y_train)

# Making predictions
y_pred = model.predict(X_test)

# Evaluating the model
print("Confusion Matrix:")
print(confusion_matrix(y_test, y_pred))
print("\nAccuracy Score:", accuracy_score(y_test, y_pred))
print("\nClassification Report:")
print(classification_report(y_test, y_pred))

# After calculating feature importances
importances = model.feature_importances_
feature_importance_df = pd.DataFrame({'Feature': X.columns, 'Importance': importances})

# Sort the DataFrame by importance
feature_importance_df = feature_importance_df.sort_values(by='Importance', ascending=False)

# Limit to top N features for clarity
top_n = 15  
top_features = feature_importance_df.head(top_n)

# Improved Visualization
plt.figure(figsize=(10, 6))
sns.barplot(x='Importance', y='Feature', data=top_features, palette='viridis')
plt.title('Top Feature Importances for Predicting High Spenders')
plt.xlabel('Importance Score')
plt.ylabel('Features')
plt.xticks(rotation=45)
plt.tight_layout() 
plt.show()

Confusion Matrix:
[[156   0]
 [  2 135]]

Accuracy Score: 0.9931740614334471

Classification Report:
              precision    recall  f1-score   support

           0       0.99      1.00      0.99       156
           1       1.00      0.99      0.99       137

    accuracy                           0.99       293
   macro avg       0.99      0.99      0.99       293
weighted avg       0.99      0.99      0.99       293


pip install lifelines

Requirement already satisfied: lifelines in c:\users\hp\documents\python scripts\lib\site-packages (0.29.0)
Requirement already satisfied: pandas>=2.1 in c:\users\hp\documents\python scripts\lib\site-packages (from lifelines) (2.1.4)
Requirement already satisfied: matplotlib>=3.0 in c:\users\hp\documents\python scripts\lib\site-packages (from lifelines) (3.7.0)
Requirement already satisfied: numpy<2.0,>=1.14.0 in c:\users\hp\documents\python scripts\lib\site-packages (from lifelines) (1.23.5)
Requirement already satisfied: autograd>=1.5 in c:\users\hp\documents\python scripts\lib\site-packages (from lifelines) (1.7.0)
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pip install pandas numpy scipy statsmodels

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# Statistical analysis part 1
import pandas as pd
import numpy as np
from scipy import stats
import statsmodels.api as sm


# 1. Chi-Square Test for Independence
# Example: Testing if 'Gender' and 'Favorite Brand' are independent
contingency_table = pd.crosstab(df['Gender'], df['Favorite Brand'])
chi2, p, dof, expected = stats.chi2_contingency(contingency_table)
print(f"Chi-square test: chi2 = {chi2}, p-value = {p}")

# 2. ANOVA Test
# Example: Checking if there's a significant difference in 'Avg Spend per Purchase' based on 'Occupation'
anova_data = [group['Avg Spend per Purchase ($)'].values for name, group in df.groupby('Occupation')]
anova_result = stats.f_oneway(*anova_data)
print(f"ANOVA test: F-statistic = {anova_result.statistic}, p-value = {anova_result.pvalue}")

# 3. T-Test
# Example: Compare 'Avg Spend per Purchase' between 'Male' and 'Female'
male_spend = df[df['Gender'] == 'Male']['Avg Spend per Purchase ($)']
female_spend = df[df['Gender'] == 'Female']['Avg Spend per Purchase ($)']
t_stat, p_val = stats.ttest_ind(male_spend, female_spend)
print(f"T-test: t-statistic = {t_stat}, p-value = {p_val}")

# 4. Mann-Whitney U Test
# Example: Comparing 'Satisfaction Rating' between 'Influencer Follow' groups
influencer_yes = df[df['Influencer Follow (Yes/No)'] == 'Yes']['Satisfaction Rating (1-5)']
influencer_no = df[df['Influencer Follow (Yes/No)'] == 'No']['Satisfaction Rating (1-5)']
u_stat, p_val_mw = stats.mannwhitneyu(influencer_yes, influencer_no)
print(f"Mann-Whitney U Test: U-statistic = {u_stat}, p-value = {p_val_mw}")

# 5. Pearson and Spearman Correlation
# Example: Correlation between 'Income ($)' and 'Avg Spend per Purchase ($)'
pearson_corr, pearson_p = stats.pearsonr(df['Income ($)'], df['Avg Spend per Purchase ($)'])
spearman_corr, spearman_p = stats.spearmanr(df['Income ($)'], df['Avg Spend per Purchase ($)'])
print(f"Pearson correlation: coefficient = {pearson_corr}, p-value = {pearson_p}")
print(f"Spearman correlation: coefficient = {spearman_corr}, p-value = {spearman_p}")

# 6. Confidence Interval for 'Avg Spend per Purchase ($)'
confidence_level = 0.95
degrees_freedom = len(df['Avg Spend per Purchase ($)']) - 1
sample_mean = np.mean(df['Avg Spend per Purchase ($)'])
sample_standard_error = stats.sem(df['Avg Spend per Purchase ($)'])
confidence_interval = stats.t.ppf((1 + confidence_level) / 2, degrees_freedom) * sample_standard_error
lower_bound = sample_mean - confidence_interval
upper_bound = sample_mean + confidence_interval
print(f"Confidence interval for Avg Spend per Purchase: ({lower_bound}, {upper_bound})")

Chi-square test: chi2 = 619.366872777155, p-value = 2.675258416299257e-113
ANOVA test: F-statistic = 182.6882018667821, p-value = 0.0
T-test: t-statistic = -1.7266641340714068, p-value = 0.08454507563320537
Mann-Whitney U Test: U-statistic = 144384.0, p-value = 6.956107136638077e-31
Pearson correlation: coefficient = 0.9061931627600043, p-value = 0.0
Spearman correlation: coefficient = 0.90765793013001, p-value = 0.0
Confidence interval for Avg Spend per Purchase: (-0.06284709956986993, 0.06284709956986999)


# Statistical analysis part 2
import pandas as pd
import numpy as np
import statsmodels.api as sm
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import seaborn as sns
from lifelines import KaplanMeierFitter


# Drop the non-numeric columns (like Consumer ID)
df_numeric = df.select_dtypes(include=[np.number])

# Descriptive Statistics
print("Descriptive Statistics:")
print(df_numeric.describe())

# Mean, Median, Mode, Std Dev, Variance, Range
mean = df_numeric.mean()
median = df_numeric.median()
mode = df_numeric.mode().iloc[0]  # mode() returns a DataFrame
std_dev = df_numeric.std()
variance = df_numeric.var()
data_range = df_numeric.max() - df_numeric.min()

print("\nMean:\n", mean)
print("\nMedian:\n", median)
print("\nMode:\n", mode)
print("\nStandard Deviation:\n", std_dev)
print("\nVariance:\n", variance)
print("\nRange:\n", data_range)

# Correlation Analysis
correlation_matrix = df_numeric.corr()
print("\nCorrelation Matrix:\n", correlation_matrix)

# Regression Analysis
# Assuming 'target' is the dependent variable and the rest are independent variables
if 'target' in df.columns:
    X = df_numeric.drop('target', axis=1, errors='ignore')  # Avoid KeyError if 'target' is not present
    y = df['target']
    X = sm.add_constant(X)  # Add constant term for intercept
    model = sm.OLS(y, X).fit()
    print("\nRegression Results:\n", model.summary())

# Multivariate Analysis
# PCA
scaler = StandardScaler()
df_scaled = scaler.fit_transform(df_numeric)  # Scale numeric columns
pca = PCA(n_components=2)  # Change n_components as needed
principal_components = pca.fit_transform(df_scaled)

# Create a DataFrame for PCA results
pca_df = pd.DataFrame(data=principal_components, columns=['PC1', 'PC2'])

# K-Means Clustering
kmeans = KMeans(n_clusters=3)  # Change n_clusters as needed
df['Cluster'] = kmeans.fit_predict(df_scaled)

# Add the Cluster assignments to the PCA DataFrame
pca_df['Cluster'] = df['Cluster']

# Visualization of PCA
plt.figure(figsize=(10, 6))
sns.scatterplot(x='PC1', y='PC2', data=pca_df)
plt.title('PCA Result')
plt.xlabel('Principal Component 1')
plt.ylabel('Principal Component 2')
plt.show()

# Visualization of K-Means Clustering
plt.figure(figsize=(10, 6))
sns.scatterplot(x='PC1', y='PC2', hue='Cluster', data=pca_df, palette='viridis')
plt.title('K-Means Clustering')
plt.xlabel('Principal Component 1')
plt.ylabel('Principal Component 2')
plt.legend(title='Cluster')
plt.show()

# Time Series Analysis (Example)
# Assuming you have a datetime column 'date' and a value column 'value'
if 'date' in df.columns and 'value' in df.columns:
    df['date'] = pd.to_datetime(df['date'])  # Ensure 'date' is datetime
    df.set_index('date', inplace=True)
    df['value'].plot(title='Time Series Analysis')
    plt.xlabel('Date')
    plt.ylabel('Value')
    plt.show()

# Survival Analysis (Example)
# Assuming 'duration' is the time until event and 'event' is whether the event occurred (1) or not (0)
if 'duration' in df.columns and 'event' in df.columns:
    kmf = KaplanMeierFitter()
    kmf.fit(durations=df['duration'], event_observed=df['event'])
    kmf.plot(title='Survival Analysis')
    plt.xlabel('Duration')
    plt.ylabel('Survival Probability')
    plt.show()

print("\nStatistical analysis completed.")

Descriptive Statistics:
                ID    Income ($)  Purchase Frequency (per month)  \
count   976.000000  9.760000e+02                    9.760000e+02   
mean    503.081967 -2.329648e-16                    1.274026e-16   
std     288.281724  1.000513e+00                    1.000513e+00   
min       1.000000 -1.778798e+00                   -1.109663e+00   
25%     253.750000 -1.289059e+00                   -1.109663e+00   
50%     497.500000  4.815371e-01                   -4.163011e-01   
75%     756.250000  8.582596e-01                    9.704221e-01   
max    1000.000000  1.234982e+00                    1.663784e+00   

       Avg Spend per Purchase ($)  Return Rate (%)  Satisfaction Rating (1-5)  \
count                9.760000e+02       976.000000                 976.000000   
mean                 2.912060e-17         4.628074                   4.521311   
std                  1.000513e+00         2.602561                   0.371134   
min                 -1.505264e+00         0.000000                   4.000000   
25%                 -1.010149e+00         3.000000                   4.200000   
50%                  2.276378e-01         5.000000                   4.500000   
75%                  6.608633e-01         6.000000                   4.900000   
max                  2.208097e+00        12.000000                   5.000000   

       Discounts Effectiveness (1-5)  
count                     976.000000  
mean                        4.143443  
std                         0.822702  
min                         2.000000  
25%                         4.000000  
50%                         4.000000  
75%                         5.000000  
max                         6.000000  

Mean:
 ID                                5.030820e+02
Income ($)                       -2.329648e-16
Purchase Frequency (per month)    1.274026e-16
Avg Spend per Purchase ($)        2.912060e-17
Return Rate (%)                   4.628074e+00
Satisfaction Rating (1-5)         4.521311e+00
Discounts Effectiveness (1-5)     4.143443e+00
dtype: float64

Median:
 ID                                497.500000
Income ($)                          0.481537
Purchase Frequency (per month)     -0.416301
Avg Spend per Purchase ($)          0.227638
Return Rate (%)                     5.000000
Satisfaction Rating (1-5)           4.500000
Discounts Effectiveness (1-5)       4.000000
dtype: float64

Mode:
 ID                                1.000000
Income ($)                       -1.439748
Purchase Frequency (per month)   -1.109663
Avg Spend per Purchase ($)        0.557714
Return Rate (%)                   5.000000
Satisfaction Rating (1-5)         5.000000
Discounts Effectiveness (1-5)     5.000000
Name: 0, dtype: float64

Standard Deviation:
 ID                                288.281724
Income ($)                          1.000513
Purchase Frequency (per month)      1.000513
Avg Spend per Purchase ($)          1.000513
Return Rate (%)                     2.602561
Satisfaction Rating (1-5)           0.371134
Discounts Effectiveness (1-5)       0.822702
dtype: float64

Variance:
 ID                                83106.352249
Income ($)                            1.001026
Purchase Frequency (per month)        1.001026
Avg Spend per Purchase ($)            1.001026
Return Rate (%)                       6.773324
Satisfaction Rating (1-5)             0.137740
Discounts Effectiveness (1-5)         0.676839
dtype: float64

Range:
 ID                                999.000000
Income ($)                          3.013780
Purchase Frequency (per month)      2.773446
Avg Spend per Purchase ($)          3.713361
Return Rate (%)                    12.000000
Satisfaction Rating (1-5)           1.000000
Discounts Effectiveness (1-5)       4.000000
dtype: float64

Correlation Matrix:
                                       ID  Income ($)  \
ID                              1.000000    0.136842   
Income ($)                      0.136842    1.000000   
Purchase Frequency (per month)  0.049595   -0.878946   
Avg Spend per Purchase ($)      0.297685    0.906193   
Return Rate (%)                -0.710113   -0.140232   
Satisfaction Rating (1-5)       0.016423    0.719211   
Discounts Effectiveness (1-5)   0.280230   -0.171169   

                                Purchase Frequency (per month)  \
ID                                                    0.049595   
Income ($)                                           -0.878946   
Purchase Frequency (per month)                        1.000000   
Avg Spend per Purchase ($)                           -0.838247   
Return Rate (%)                                      -0.020224   
Satisfaction Rating (1-5)                            -0.632360   
Discounts Effectiveness (1-5)                         0.215939   

                                Avg Spend per Purchase ($)  Return Rate (%)  \
ID                                                0.297685        -0.710113   
Income ($)                                        0.906193        -0.140232   
Purchase Frequency (per month)                   -0.838247        -0.020224   
Avg Spend per Purchase ($)                        1.000000        -0.245171   
Return Rate (%)                                  -0.245171         1.000000   
Satisfaction Rating (1-5)                         0.715045        -0.123349   
Discounts Effectiveness (1-5)                    -0.002488        -0.354919   

                                Satisfaction Rating (1-5)  \
ID                                               0.016423   
Income ($)                                       0.719211   
Purchase Frequency (per month)                  -0.632360   
Avg Spend per Purchase ($)                       0.715045   
Return Rate (%)                                 -0.123349   
Satisfaction Rating (1-5)                        1.000000   
Discounts Effectiveness (1-5)                    0.205296   

                                Discounts Effectiveness (1-5)  
ID                                                   0.280230  
Income ($)                                          -0.171169  
Purchase Frequency (per month)                       0.215939  
Avg Spend per Purchase ($)                          -0.002488  
Return Rate (%)                                     -0.354919  
Satisfaction Rating (1-5)                            0.205296  
Discounts Effectiveness (1-5)                        1.000000

Statistical analysis completed.


## Clustering and Segmentation
import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt


# Drop the non-numeric columns (like Consumer ID)
df_numeric = df.select_dtypes(include=[np.number])

# Scale the numeric data
scaler = StandardScaler()
df_scaled = scaler.fit_transform(df_numeric)

# Elbow Method Analysis
inertia = []  # List to store the inertia values

# Test different cluster sizes
for n_clusters in range(1, 11):  # Testing 1 to 10 clusters
    kmeans = KMeans(n_clusters=n_clusters, random_state=42)
    kmeans.fit(df_scaled)
    inertia.append(kmeans.inertia_)

# Plotting the Elbow Curve
plt.figure(figsize=(10, 6))
plt.plot(range(1, 11), inertia, marker='o')
plt.title('Elbow Method for Optimal k')
plt.xlabel('Number of Clusters (k)')
plt.ylabel('Inertia')
plt.xticks(range(1, 11))
plt.grid()
plt.show()


# Market basket analysis
import pandas as pd
import numpy as np
from mlxtend.frequent_patterns import apriori, association_rules
import matplotlib.pyplot as plt
import seaborn as sns

# Create a sample dataset
num_entries = 50
num_categories = 5  # Each ID can purchase multiple categories
categories = ['Sneakers', 'T-Shirt', 'Blouse', 'Shorts', 'Handbag', 'Suit', 
              'Dress', 'Jacket', 'Skirt', 'Hoodie', 'Jeans', 'Blazer',
              'Crop Top', 'Chinos', 'Tie', 'Top']

# Randomly select multiple categories for each ID
np.random.seed(0)  # For reproducibility
data = {
    'ID': np.repeat(np.arange(1, num_entries + 1), num_categories),
    'Last Purchase Category': np.random.choice(categories, size=num_entries * num_categories)
}

# Step 1: Convert the data into a DataFrame
df = pd.DataFrame(data)

# Step 2: Create a one-hot encoded DataFrame
basket = df.pivot_table(index='ID', columns='Last Purchase Category', aggfunc='size', fill_value=0)
# Convert counts to binary presence
basket[basket > 0] = 1

# Step 3: Generate frequent itemsets with lower support
min_support = 0.01  # Lowered minimum support threshold
frequent_itemsets = apriori(basket, min_support=min_support, use_colnames=True)
print("Frequent Itemsets:\n", frequent_itemsets)

# Step 4: Generate association rules with a lower confidence threshold
min_threshold = 0.2  # Lowered confidence threshold
rules = association_rules(frequent_itemsets, metric="confidence", min_threshold=min_threshold)
print("\nAssociation Rules:\n", rules)

# Step 5: Visualize the one-hot encoded data
plt.figure(figsize=(12, 8))
sns.heatmap(basket, cmap='YlGnBu', cbar=True, annot=True)
plt.title("One-Hot Encoded Data Heatmap")
plt.xlabel("Last Purchase Category")
plt.ylabel("ID")
plt.show()

C:\Users\hp\Documents\Python Scripts\lib\site-packages\mlxtend\frequent_patterns\fpcommon.py:109: DeprecationWarning:

DataFrames with non-bool types result in worse computationalperformance and their support might be discontinued in the future.Please use a DataFrame with bool type

Frequent Itemsets:
      support                                     itemsets
0       0.24                                     (Blazer)
1       0.30                                     (Blouse)
2       0.26                                     (Chinos)
3       0.18                                   (Crop Top)
4       0.18                                      (Dress)
..       ...                                          ...
485     0.02      (Jeans, Handbag, Sneakers, Top, Jacket)
486     0.02           (Shorts, Tie, Jeans, Handbag, Top)
487     0.02  (Shorts, T-Shirt, Handbag, Sneakers, Skirt)
488     0.02        (Suit, Sneakers, Top, Jacket, Hoodie)
489     0.02    (Shorts, Suit, T-Shirt, Sneakers, Hoodie)

[490 rows x 2 columns]

Association Rules:
               antecedents                  consequents  antecedent support  \
0                (Blazer)                     (Blouse)                0.24   
1                (Blouse)                     (Blazer)                0.30   
2                (Chinos)                     (Blazer)                0.26   
3                (Blazer)                     (Chinos)                0.24   
4                (Hoodie)                     (Blazer)                0.28   
...                   ...                          ...                 ...   
2058       (Shorts, Suit)  (Sneakers, Hoodie, T-Shirt)                0.06   
2059    (Shorts, T-Shirt)     (Sneakers, Hoodie, Suit)                0.06   
2060     (Shorts, Hoodie)    (Sneakers, Suit, T-Shirt)                0.08   
2061      (Suit, T-Shirt)   (Shorts, Hoodie, Sneakers)                0.04   
2062  (Sneakers, T-Shirt)       (Shorts, Hoodie, Suit)                0.08   

      consequent support  support  confidence       lift  leverage  \
0                   0.30     0.14    0.583333   1.944444    0.0680   
1                   0.24     0.14    0.466667   1.944444    0.0680   
2                   0.24     0.06    0.230769   0.961538   -0.0024   
3                   0.26     0.06    0.250000   0.961538   -0.0024   
4                   0.24     0.10    0.357143   1.488095    0.0328   
...                  ...      ...         ...        ...       ...   
2058                0.04     0.02    0.333333   8.333333    0.0176   
2059                0.06     0.02    0.333333   5.555556    0.0164   
2060                0.02     0.02    0.250000  12.500000    0.0184   
2061                0.04     0.02    0.500000  12.500000    0.0184   
2062                0.02     0.02    0.250000  12.500000    0.0184   

      conviction  zhangs_metric  
0       1.680000       0.639098  
1       1.425000       0.693878  
2       0.988000      -0.051282  
3       0.986667      -0.050000  
4       1.182222       0.455556  
...          ...            ...  
2058    1.440000       0.936170  
2059    1.410000       0.872340  
2060    1.306667       1.000000  
2061    1.920000       0.958333  
2062    1.306667       1.000000  

[2063 rows x 10 columns]

Data Analysis on E-commerce Fashion Data¶

Data Cleaning and Preprocessing¶

Normalization¶

When to Normalize:¶

When It Might Not Be Necessary:¶

Funnel Analysis¶

Funnel analysis is a method used to track the steps customers take in a conversion process, identifying where potential customers drop off. This is particularly useful for businesses to understand user behavior, optimize marketing efforts, and improve the overall conversion rate.¶

Funnel analysis typically involves several stages that lead to a final goal, such as making a purchase or signing up for a service. Each stage represents a step in the user journey, and analyzing these stages helps pinpoint where users are lost and where improvements can be made.¶

For this dataset, let's consider the following funnel stages:¶

Conversion Rates: The conversion rates are calculated relative to the first stage.¶

Exploratory Data Analysis (EDA)¶

Components of EDA¶

Importance of EDA¶

Feature Engineering¶

Key Aspects of Feature Engineering¶

Importance of Feature Engineering¶

Statistical analysis¶

Explanation¶

Summary of Statistical Analysis Results¶

Insights¶

Summary of Descriptive Statistics¶

Clustering and Segmentation¶

Clustering¶

Segmentation Analysis¶

Key Differences¶

Elbow method¶

How It Works:¶

Steps to Use the Elbow Method:¶

Market Basket Analysis¶

Key Concepts¶

Applications¶

Techniques¶

Summary of Output¶

Frequent Itemsets¶

Association Rules¶

Insights¶

	ID	Consumer ID	Gender	Age Group	Income ($)	Occupation	Preferred Style	Favorite Brand	Purchase Frequency (per month)	Avg Spend per Purchase ($)	...	Color Preference	Online Shopping Frequency	Return Rate (%)	Satisfaction Rating (1-5)	Influencer Follow (Yes/No)	Discounts Effectiveness (1-5)	Sustainable Preference (Yes/No)	Last Purchase Category	Last Purchase Date	Review Written (Yes/No)
0	1	C001	Female	18-24	30000	Student	Casual	Nike	3	50	...	Blue	Weekly	5	4.5	Yes	5	Yes	Sneakers	2024-09-15	Yes
1	2	C002	Male	25-34	60000	Engineer	Smart Casual	Zara	2	70	...	Black	Monthly	10	4.2	No	4	No	T-Shirt	2024-08-20	No
2	3	C003	Female	35-44	45000	Teacher	Business Casual	H&M	1	100	...	Red	Occasionally	15	4.8	Yes	3	Yes	Blouse	2024-07-30	Yes
3	4	C004	Male	18-24	25000	Student	Casual	Adidas	4	40	...	Green	Weekly	5	4.3	Yes	5	No	Shorts	2024-09-10	Yes
4	5	C005	Female	25-34	70000	Marketing	Trendy	Gucci	1	150	...	Pink	Monthly	20	4.6	No	2	Yes	Handbag	2024-08-05	No
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
995	996	C996	Male	35-44	91000	Business Analyst	Smart Casual	Urban Outfitters	1	210	...	Black	Occasionally	0	5.0	Yes	5	Yes	Accessories	2025-12-13	No
996	997	C997	Female	18-24	34000	Intern	Casual	H&M	4	55	...	Light Blue	Weekly	2	4.0	Yes	4	No	Swimwear	2025-12-14	Yes
997	998	C998	Male	25-34	80000	Software Developer	Trendy	ASOS	3	130	...	Coral	Monthly	1	5.0	Yes	5	Yes	Jackets	2025-12-14	No
998	999	C999	Female	35-44	85000	Marketing Manager	Business Casual	J.Crew	1	220	...	Green	Occasionally	3	5.0	Yes	5	Yes	Outerwear	2025-12-15	Yes
999	1000	C1000	Male	18-24	29000	Student	Casual	Forever 21	5	30	...	Yellow	Weekly	2	4.0	Yes	4	No	T-Shirts	2025-12-15	No